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## chromeDinosaurCheater.js
// Keep a copy of original runner
var originalRunner=Runner.prototype.gameOver
// Overwrite gameover function so we don't die
Runner.prototype.gameOver=function(){console.log("Its over when I say its over")}
// Super speed run
Runner.instance_.setSpeed(15000)
// Change back to original game when you are sick of cheating
Runner.prototype.gameOver=originalRunner

## contemplative-llms.txt
You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

## llm-wiki.md

      
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                karpathy
                / llm-wiki.md
            
            
              Created
              April 4, 2026 16:25
            
              
                llm-wiki
              
          
    LLM Wiki

A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

  
## idiomatic_go_v2.md

      
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                ashokallu
                / idiomatic_go_v2.md
            
            
              Last active
              May 18, 2026 01:44
            
              
                Idiomatic Go Programming Guide 2025+ - Gold Standard Edition
              
          
    Idiomatic Go Programming Guide 2025+ - Gold Standard Edition


"Go is about making software engineering more effective, not just making programmers more productive." - The Go Team

Target Go Version: Go 1.23+ (examples align with 1.23–1.24 features)

Executive Summary


## VB-NANOCLAW-MEMORY-OBSI-WIKI-PUBLIC.md

      
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                VivianBalakrishnan
                / VB-NANOCLAW-MEMORY-OBSI-WIKI-PUBLIC.md
            
            
              Created
              April 24, 2026 09:34
            
              
                NanoClaw — Personal Claude Assistant (second brain for a diplomat)
              
          
    NanoClaw — Personal Claude Assistant

A self-hosted, compounding-memory AI assistant running on a Raspberry Pi.

What Is This?

NanoClaw is a personal AI assistant built on Anthropic's Claude that runs entirely on a Raspberry Pi. It connects to messaging channels (WhatsApp, Telegram, Slack, Discord), processes voice and images, schedules recurring tasks, and — unlike a standard chatbot — accumulates knowledge over time through a structured memory system.


## optimize_ppo.py
"""Optuna example that optimizes the hyperparameters of
a reinforcement learning agent using PPO implementation from Stable-Baselines3
on a Gymnasium environment.

This is a simplified version of what can be found in https://github.com/DLR-RM/rl-baselines3-zoo.

You can run this example as follows:
    $ python optimize_ppo.py

"""
	// Keep a copy of original runner
	var originalRunner=Runner.prototype.gameOver
	// Overwrite gameover function so we don't die
	Runner.prototype.gameOver=function(){console.log("Its over when I say its over")}
	// Super speed run
	Runner.instance_.setSpeed(15000)
	// Change back to original game when you are sick of cheating
	Runner.prototype.gameOver=originalRunner
	You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

	## Core Principles

	1. EXPLORATION OVER CONCLUSION
	- Never rush to conclusions
	- Keep exploring until a solution emerges naturally from the evidence
	- If uncertain, continue reasoning indefinitely
	- Question every assumption and inference
	"""Optuna example that optimizes the hyperparameters of
	a reinforcement learning agent using PPO implementation from Stable-Baselines3
	on a Gymnasium environment.

	This is a simplified version of what can be found in https://github.com/DLR-RM/rl-baselines3-zoo.

	You can run this example as follows:
	$ python optimize_ppo.py

	"""